Full-Sum Decoding for Hybrid Hmm Based Speech Recognition Using LSTM Language Model

IEEE International Conference on Acoustics Speech and Signal Processing, pp. 7834–7838

Abstract

In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should be applied in decoding. This motivates to reconsider full summation over the HMM-state sequences instead of Viterbi approximation in decoding. We explore the potential gain from more accurate probabilities in terms of decision making and apply the full-sum decoding with a modified prefix-tree search framework. The proposed full-sum decoding is evaluated on both Switchboard and Librispeech corpora. Different models using CE and sMBR training criteria are used. Additionally, both MAP and confusion network decoding as approximated variants of general Bayes decision rule are evaluated. Consistent improvements over strong baselines are achieved in almost all cases without extra cost. We also discuss tuning effort, efficiency and some limitations of full-sum decoding.

Authors 3

1. Wei Guo Zhou Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University, Aachen, Germany

2. Ralf Schlüter Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University, Aachen, Germany

3. Hermann Ney Aachen Human Language Technology and Pattern Recognition

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition, RWTH Aachen University, Aachen, Germany